Assistant Vice President, Structured Finance Analytics | Data Analytics, Big Data & Credit Ratings
- Compensation
- Not disclosed
- Location
- Madrid
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data analytics
- Posted
Skills
Must have
- Minimum 3 years of structured finance experience.
- Demonstrated experience leading complex data, analytics, process-improvement or technology-enabled change initiatives from concept to implementation.
- Experience integrating large, structured datasets into business or analytical processes, with a strong understanding of data quality, controls and documentation.
- Working knowledge of data-analysis and visualisation tools such as SQL, Tableau or Python, together with the confidence and curiosity to develop further technical capability.
- Strong project-management skills, including the ability to define scope, prioritise work, manage dependencies and deliver through multiple stakeholders.
- Excellent listening, written and verbal communication skills, with the ability to translate business needs into clear technical requirements and explain complex topics to non-technical audiences.
- Strong interpersonal skills, with the ability to motivate colleagues, build trust and coordinate delivery across functions, locations and levels of seniority.
- A rigorous approach to quality assurance, problem solving and continuous improvement.
- A proactive, adaptable and collaborative mindset, with enthusiasm for using data, automation and approved AI tools to improve analytical outcomes.
Full description
About the role
Morningstar DBRS' Structured Finance Analytics team supports Global Structured Finance (GSF) rating teams across North America, Europe and APAC. Within this group, the Data Analytics team turns complex data into reliable analytical content, scalable workflows and decision-ready insights. Our goal is to improve operational efficiencies, reduce operational risk and enhance analytical capabilities.
We are seeking an Assistant Vice President with structured finance ratings experience and a proven ability to leverage data, technology and process innovation to drive change across Ratings, Operations and Technology teams.
You will connect structured finance knowledge with data, technology and change management: identifying high-value opportunities with rating teams, shaping requirements, guiding implementation and embedding solutions into day-to-day structured finance analytical processes.
Key initiatives include evaluating and integrating data from the Credit Data Lake into the Credit Data Hub, creating common or tailored analytical outputs using either Tableau or Python and establishing sustainable support, governance and quality-assurance frameworks.
This is a cross-functional role for someone who can lead projects, build trusted relationships with rating teams, Credit Operations and Credit Technology, and translate business needs into practical technical solutions. You will also help the team adopt approved AI tools responsibly, including GitHub Copilot and Copilot Studio, with appropriate human review and controls.
The position offers broad exposure to structured finance asset classes, jurisdictions and senior stakeholders globally, together with the opportunity to influence how data, technology and automation enhance analytical quality, operational effectiveness and innovation across Global Structured Finance.
Responsibilities
- Lead the design, prioritisation and delivery of strategic data analytics initiatives across Global Structured Finance, from discovery and requirements through implementation and post-launch support.
- Partner with rating teams to understand analytical needs, identify scalable solutions and support adoption across asset classes and regions.
- Work closely with Credit Operations, Credit Technology and other partners to define requirements, coordinate dependencies, manage risks and deliver agreed outcomes.
- Support the development of other Data Analytics colleagues by contributing to objective setting, providing coaching and feedback, and creating opportunities for growth and ownership.
- Evaluate large, structured datasets; assess data quality and fitness for purpose; and guide their integration into governed analytical workflows.
- Development of dashboards, automated analysis and presentation layers using Tableau, Python or other approved tools.